Source: PBS

The unpredictability of the outcomes in today’s decision models often arises from the inability to capture the uncertainty factors linked to these models’ “behavior” in a business context. By introducing machine learning algorithms to decision-making processes, a new field called “decision intelligence” is emerging to create strong decision models in a wide range of processes.

What is decision intelligence?

Decision intelligence is a trending field that contains a range of decision-making methods to design, model, align, execute, and track decision models and processes. The implementation offers a structure for organizational decision-making and processes with the integration of machine learning algorithms. The main idea is that decisions are based on our perception of how actions lead to outcomes.

Here is Wikipedia’s definition of decision intelligence:

Decision intelligence is a discipline for analyzing this chain of cause and effect, and decision modeling is a visual language for representing these chains.

Decision intelligence is a field that also includes decision management and decision support, as well as methods like descriptive, diagnostic, and predictive analytics.

How do intelligent decision models work?

Businesses have complicated adaptive systems. Unsuccessful outcomes often arise from the discrepancies between the sophistication of organizational decision-making practices and the complexity of the situations in which those decisions need to be taken. By following the steps below, intelligent decision models aim to help businesses for profitable decision making.


The models start with collecting all relevant information. This data can be historical, transactional, sensory, behavioral, attitudinal, structured, unstructured, transient or persistent, external, or internal to businesses. Any piece of information can help for reconstructing the outcome, informing other impacted mechanisms, or improving processes.


With all the collected data, models bring a clear understanding of the situation and construct possible actions.


This step includes the generation of alternative actions by considering existing business capabilities. These actions must explain causalities that lead to alternative scenarios because businesses can’t always foresee the complete picture during decision processes. 


Considering the decision time, the model might run out of options due to the high complexity of the situation. Emerging techniques benefiting game theory methods, sophisticated system modeling, and dynamic agent-based collaboration methods help address the blind spots of the model. The idea is to provide the decision-maker with a range of executable actions that can be implemented quickly.


In the end, the decision model chooses and takes a particular action. After execution, this part is also responsible for measuring the impact of the taken action to improve the model.

What are the different types of decision models?

There are three primary levels of decision models:

Human-based Decisions

In these decision models, AI systems provide insights and data visualization for humans. However, it is not directly connected to the decision processes, and humans make decisions.

Machine-based Decisions

AI systems make decisions independently in this type of model. While it can be argued that humans are still at the base of autonomous systems, AI systems can develop behaviors that are not predicted by their programmers (apart from programming errors). Examples of machine-based decisions can be swarm networks and their evolving practices.

Hybrid Decisions

Both humans and AI systems work together to achieve an outcome. This decision model can make recommendations or even take action for humans. The proportion of the decision-making process handled by either humans or machines can vary widely, but the collaboration of AI systems and humans provides the basis of the outcome.

What are the principles for sustainable decision models?

According to Gartner, sustainable decision models must fulfill the following three principles:


Every decision should contribute, directly or indirectly, to the outcome. For example, in customer relationships, relevance is often connected with the optimal action that can be taken to obtain, retain, or extend customer relationships.


The growing popularity of machine learning techniques brings the tradeoff between using accuracy and explainability. Businesses can prefer using black-box models at the price of understanding how these models work. Decision traceability, intelligibility, and explicit dependencies are vital for business stakeholders, customers, and regulatory approvals.


Ensuring the stability of decisions in the light of complex and continuously evolving processes is essential to their reliability. By stability, we refer to the ability to detect harmful biases and security breaches while being able to fail gracefully when encountering uncertain situations.

What are the main benefits?

The main benefits include:

  • Making more accurate decisions that provide better outcomes
  • Making faster decisions
  • Eliminating errors like biases
  • Accommodating the benefits of human judgments like intuitions

How will decision intelligence evolve in the future?

When businesses have reliable data analyses, recommendations, and follow-ups through AI systems, they make better decisions. The reason for integrating AI systems in businesses relies on the growing amount of available data. Gartner indicates that we will have 800% more data by the end of 2020, and 80% of this is unstructured data that consists of images, emails, voice records, etc. 

While the human force wouldn’t be able to process all this data, decision intelligence is a solution to handle this increase by the help of improving machine learning algorithms.

As human intuition in the decision-making process wouldn’t be eliminated, machine learning algorithms will provide valuable insights and support. In the future, decision intelligence might impact businesses in two different ways:

  • With higher computational power, AI systems can support managers to make fast, informed, and accurate decisions by offering the most profitable options.
  • AI agents can make decisions on their own, with the attributes and capabilities of a person running a department.

What are the example case studies demonstrating modern decision intelligence?

Decision intelligence solutions can be implemented in a wide range of use cases under different industries. Decision intelligence incorporates both human and machine contributions to arrive at optimal decisions. However, it is hard to identify case studies that incorporate this since most case studies are published by software vendors and highlight how software contributes to better real-time decision making. That is why these case studies focus on machines making real-time decisions:

Logistics Optimization

Because bulk tankers are specialized in a small set of products, shippers need to contact small logistic providers to handle their transportation needs, and this situation causes higher supplier management costs for the company. By using IBM’s Hybrid Cloud solution, this leading bulk tanker transportation company decided to optimize truck routes in real-time. As a result, it eliminated miles of unnecessary driving and saved millions of dollars.

Demand Forecasting

Red Eléctrica de España, the Canary Islands’ electricity company, needs to balance supply and demand for electricity while integrating renewable energy sources like wind and solar power into their production systems. The company decides to use IBM’s Hybrid Cloud solution for electricity demand forecasting and optimize their electricity production. As a result, electricity demand forecasts are done much faster with higher accuracy levels.

Discovering Cause-and-Effect Links

A banking company with locations in 53 countries needed to upgrade its telecommunication technology. However, it seems highly costly because an action taken in one database has consequences on another one, and the company can’t easily connect these cause-and-effect chain links. With a decision intelligence solution, the company gained an understanding of these chains of events and minimized their costs while upgrading its telecommunication technology.

What are the leading vendors in decision intelligence?

Although most solutions are provided by AI, Business Intelligence, and Cloud Computing vendors, we can present a shortlist of vendors that can provide decision intelligence solutions for your business.

  • Busigence
  • Google Cloud Platform
  • IBM Hybrid Cloud
  • Oracle Business Intelligence
  • Quantellia
  • Urbint
  • Xylem

If you have questions on decision intelligence tools, feel free to contact us:

Let us find the right vendor for your business

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